Research on data driven dynamic mechanism of energy enterprise investment: based on system dynamics simulation - Yongfeng Qiao; Hongtao Zhu; Yue Zhu (Energy Informatics)
Abstract Under the background of global energy transformation and the integration of digital economy, energy enterprises’ digital investment faces the challenges of uncertain return cycle and lack of data asset pricing mechanism. By constructing a system dynamics model, this paper reveals the dynamic mechanism of data-driven digital investment decision-making of energy enterprises. The research shows that: the value of data assets forms a self reinforcing cycle through the return reinvestment loop, and its scale expansion is regulated by the dynamic balance between the cost constraint and the value inhibition loop; The improvement of market risk perception, the robustness of the trading market, the increase of energy policy intensity and the weakening of peer competition can significantly improve the cumulative profits of enterprises; Adaptive investment strategy has more advantages than fixed investment strategy, but the timing of strategy transformation needs to be accurately controlled. The simulation results provide a basis for enterprises to optimize the data investment path. It is suggested to build a data-driven dynamic investment system, deepen the operation of data assets, and call on the policy side to improve the data factor market system and incentive measures, so as to jointly promote the strategic transformation of energy enterprises to data centers.